Project: Advancing the SEEA Experimental Ecosystem Accounting€¦ · landscape In spatial models:...
Transcript of Project: Advancing the SEEA Experimental Ecosystem Accounting€¦ · landscape In spatial models:...
System of Environmental-Economic Accounting
Biophysical Modelling (Levels 1 and 2)
Project: Advancing the SEEA
Experimental Ecosystem Accounting
System of Environmental-Economic Accounting
Contents
Beginner level (level 1) • Learning objectives
• Presentation
• Exercise sheet
• Answer sheet
Intermediate level (level 2) • Learning objectives
• Presentation
• Exercise sheet
• Answer sheet
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System of Environmental-Economic Accounting
Level 1: Learning Objectives
Understand the spatial units used in Ecosystem
Accounting and the role of biophysical modelling
in support of Ecosystem Accounting • Spatial Units
• The role of spatial and of temporal modelling
Required prior knowledge: the concept of
ecosystem services
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The three spatial Units in Ecosystem Accounts
Ecosystem Accounting
Unit (EAU) = a country,
province or watershed
Land cover/ecosystem
functional unit (LCEU) =
e.g. Deciduous forest
Basic Spatial Unit = a grid
cell / pixel
Ecosystem services flow,
ecosystem condition and
ecosystem asset needs to
be defined for every pixel
individually (i.e. mapped)
Land cover map of Area X
Scale: 1:50,000
Deciduous forest
Pine forest
System of Environmental-Economic Accounting
Biophysical modelling
Biophysical modelling for the purpose of
ecosystem accounting is required to: • Capture the spatial variability of ecosystems
• Capture the temporal variability of ecosystems
Modelling: make it as simple as possible, but not
simpler (after Einstein)
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Spatial variability in Ecosystems
Ecosystems vary across space because of
differences in altitude, soils, vegetation, climate,
etc. and because of spatial differences in the
management of ecosystems (e.g. as a function
of distance to road or settlement)
The Wetland ecosystem ‘De Wieden’, the
Netherlands consists of different zones with
different vegetation, humidity, soils, etc.
(Source: Hein et al., 2006)
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Modelling spatial variability
Modelling spatial variability is required because • ecosystem accounting aims to record flows of
ecosystems and stocks of ecosystem capital; and
• both flows and stocks can be spatially very
heterogeneous.
Flows and stock modelling usually requires the
combination of spatial datasets (maps) and
point-data(e.g. from surveys).
To combine these datasets, and to understand
ecosystem flows and assets in areas with no
data, a spatial model can be deployed.
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Modelling temporal variability
Temporal models are required to analyse ecosystem assets
The Net Present Value of an ecosystem asset is the sum of
the net, current and discounted future values generated by
the ecosystem
These values depend upon the capacity of the ecosystem to
regenerate, for instance the regrowth of the forest after
harvesting.
Temporal models reveal the regeneration/regrowth of the
ecosystem
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The increase in a biological
resource often follows an S-curve,
where growth levels approach
zero of at the carrying capacity.
System of Environmental-Economic Accounting
Exercise 1
What are the three spatial units in Ecosystem
Accounting, and what is their meaning ?
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Exercise 2
Which processes or factors determine changes
in the stock of a (renewable) ecosystem asset,
for instance a timber resource in a natural
forest?
Which of these factors requires temporal
modelling in order to analyse it?
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Level 2. Biophysical modelling
Learning objective: • To understand which types of modelling approaches
can be used for the spatial and temporal modelling of
ecosystem services in an accounting context
Prior knowledge • Biophysical modelling level 1
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Spatial modelling
Useful to estimate ecosystem services flows and
ecosystem assets across spatial units in the
landscape
In spatial models: ecosystem condition
indicators, ecosystem services flow and
ecosystem assets are defined and analysed for
each spatial unit
This requires Geographical Information
Systems, where the spatial unit is usually a pixel
(i.e. a grid cell in a grid, with specific x and y
coordinates) 14
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Four types of spatial models
Look-up tables: specific values for an ecosystem service or other variable are attributed to every pixel in a certain class, usually a land cover class.
Statistical approaches: relate ecosystem services flow, asset or condition to explanatory variables such as soils, land cover, climate, distance from a road, etc., using a statistical relation derived from survey data.
Geostatistical interpolation: techniques such as kriging rely on statistical algorithms to predict the value of un-sampled pixels on the basis of nearby pixels in combination with other characteristics of the pixel.
Process based modeling: involves predicting ecosystem services flows based on a set of environmental properties, management variables and/or other spatial data sources, based on modelling of the ecological and/or ecosystem management processes involved.
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Ecosystem services Central Kalimantan Carbon storage Model used
Look Up Tables (every land cover class is attributed a specific carbon storage value) Kriging (values are interpolated from samples)
High : 1.67 m3/ha/year
Low : 0.42 m3/ha/year
Timber production
Source: Sumarga and Hein, 2014
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Ecosystem services Central Kalimantan
Orangutan habitat Model used:
Statistical model (Maxent) (habitat suitability predicted on the basis of forest cover, distance from road, etc.) Process-based Model (primary ecosystem production minus soil respiration)
High : 8.52 ton/ha/year
Low : -23.22 ton/ha/year
Carbon sequestration
Source: Sumarga and Hein, 2014 17
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Modelling ecosystem services
Can be done in stand-alone GIS packages such
as ArcGIS or Q-GIS (freeware) or
Modelling packages such as ARIES or InVEST
Advantages of ARIES and InVEST is that some
modules are pre-defined;
Advantage of standard packages is that they are
fully flexible and not dependent upon
(knowledge of) a specific modelling program
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Question 1
Which four types of spatial models can be
distinguished?
What is the differences between them? Explain
in your own words.
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Four types of spatial models
Look-up tables: specific values are attributed to every pixel in a
certain class, usually a land cover class.
Statistical approaches: ecosystem services flow, asset or condition
is related to explanatory variables such as soils, land cover, climate,
distance form a road, etc., using a statistical relation derived from
survey data.
Geostatistical interpolation: techniques such as kriging rely on
statistical algorithms to predict the value of un-sampled pixels on the
basis of nearby pixels in combination with other characteristics of
the pixel.
Process based modeling: involves predicting ecosystem services
flows based on modelling of the ecological and/or ecosystem
management processes involved.
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Question 2
Select one or two ecosystem services relevant
for your case study area of interest • Which different sets of data are required to map this
service?
• How can these datasets be combined?
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Level 2: Biophysical Modelling
References Hein, L., Van Koppen, K., De Groot, R. S., & Van Ierland, E. C. (2006). Spatial
scales, stakeholders and the valuation of ecosystem services. Ecological Economics,
57(2), 209-228.
Sumarga, E. And Hein, L., 2014. Mapping Ecosystem Services for Land Use
Planning, the Case of Central Kalimantan. Environmental management, pp. 1-14.
Further Information • SEEA Experimental Ecosystem Accounting (2012)
• SEEA-EEA Technical Guidance (forthcoming) ▫ Detailed supporting document on “Biophysical Modelling” by
Lars Hein
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Evaluation of the training module
Please complete the evaluation form for this module
For this module
• What did you learn that you could apply in your work?
• Was the presentation clear and informative?
• Was it too simple? Too complex?
• Was there anything you did not understand?
• What additions or deletions would you suggest (recognizing that
the unit is intended for a general audience)?
• Do you have any suggestions as to how the SEEA-EEA may be
improved (concepts, principles) in this area?
System of Environmental-Economic Accounting
Acknowledgements
This project is a collaboration of The United Nations
Statistics Division, United Nations Environment
Programme and the Secretariat of the Convention on
Biological Diversity and is supported by the Government
of Norway.